Buch, Englisch, 576 Seiten, Format (B × H): 181 mm x 251 mm, Gewicht: 1160 g
ISBN: 978-0-470-02619-9
Verlag: Wiley-Blackwell
Praise from the reviews:
"Without reservation, I endorse this text as the best resource I've encountered that neatly introduces and summarizes many points I've learned through years of experience. The gems of truth found in this book will serve well those who wish to apply bioinformatics in their daily work, as well as help them advise others in this capacity." CIRCGENETICS
"This book may really help to get geneticists and bioinformaticians on 'speaking-terms'. contains some essential reading for almost any person working in the field of molecular genetics." EUROPEAN JOURNAL OF HUMAN GENETICS
". an excellent resource. this book should ensure that any researcher's skill base is maintained." GENETICAL RESEARCH
“… one of the best available and most accessible texts on bioinformatics and genetics in the postgenome age… The writing is clear, with succinct subsections within each chapter….Without reservation, I endorse this text as the best resource I’ve encountered that neatly introduces and summarizes many points I’ve learned through years of experience. The gems of truth found in this book will serve well those who wish to apply bioinformatics in their daily work, as well as help them advise others in this capacity.” CIRCULATION: CARDIOVASCULAR GENETICS
A fully revised version of the successful First Edition, this one-stop reference book enables all geneticists to improve the efficiency of their research.
The study of human genetics is moving into a challenging new era. New technologies and data resources such as the HapMap are enabling genome-wide studies, which could potentially identify most common genetic determinants of human health, disease and drug response. With these tremendous new data resources at hand, more than ever care is required in their use. Faced with the sheer volume of genetics and genomic data, bioinformatics is essential to avoid drowning true signal in noise. Considering these challenges, Bioinformatics for Geneticists, Second Edition works at multiple levels: firstly, for the occasional user who simply wants to extract or analyse specific data; secondly, at the level of the advanced user providing explanations of how and why a tool works and how it can be used to greatest effect. Finally experts from fields allied to genetics give insight into the best genomics tools and data to enhance a genetic experiment.
Hallmark Features of the Second Edition:
- Illustrates the value of bioinformatics as a constantly evolving avenue into novel approaches to study genetics
- The only book specifically addressing the bioinformatics needs of geneticists
- More than 50% of chapters are completely new contributions
- Dramatically revised content in core areas of gene and genomic characterisation, pathway analysis, SNP functional analysis and statistical genetics
- Focused on freely available tools and web-based approaches to bioinformatics analysis, suitable for novices and experienced researchers alike
Bioinformatics for Geneticists, Second Edition describes the key bioinformatics and genetic analysis processes that are needed to identify human genetic determinants. The book is based upon the combined practical experience of domain experts from academic and industrial research environments and is of interest to a broad audience, including students, researchers and clinicians working in the human genetics domain.
Autoren/Hrsg.
Fachgebiete
- Naturwissenschaften Biowissenschaften Biowissenschaften DNA und Transgene Organismen
- Naturwissenschaften Biowissenschaften Botanik Pflanzenreproduktion, Verbreitung, Genetik
- Naturwissenschaften Biowissenschaften Tierkunde / Zoologie Tiergenetik, Reproduktion
- Naturwissenschaften Biowissenschaften Biowissenschaften Genetik und Genomik (nichtmedizinisch)
Weitere Infos & Material
Foreword xi
Preface xv
Contributors xvii
Glossary xix
Section I An Introduction to Bioinformatics for The Geneticist 1
1 Bioinformatics challenges for the geneticist 3
Michael R. Barnes
1.1 Introduction 3
1.2 The role of bioinformatics in genetics research 4
1.3 Genetics in the post-genome era 5
1.4 Conclusions 12
References 15
2 Managing and manipulating genetic data 17
Karl W. Broman and Simon C. Heath
2.1 Introduction 17
2.2 Basic principles 18
2.3 Data entry and storage 20
2.4 Data manipulation 21
2.5 Examples of code 22
2.6 Resources 30
2.7 Summary 31
References 31
Section II Mastering Genes, Genomes and Genetic Variation Data 33
3 The HapMap – A haplotype map of the human genome 35
Ellen M. Brown and Bryan J. Barratt
3.1 Introduction 35
3.2 Accessing the data 38
3.3 Application of HapMap data in association studies 42
3.4 Future perspectives 54
References 54
4 Assembling a view of the human genome 59
Colin A. M. Semple
4.1 Introduction 59
4.2 Genomic sequence assembly 60
4.3 Annotation from a distance: the generalities 64
4.4 Annotation up close and personal: the specifics 70
4.5 Annotation: the next generation 78
References 80
5 Finding, delineating and analysing genes 85
Christopher Southan and Michael R. Barnes
5.1 Introduction 85
5.2 Why learn to predict and analyse genes in the complete genome era? 86
5.3 The evidence cascade for gene products 88
5.4 Dealing with the complexities of gene models 95
5.5 Locating known genes in the human genome 97
5.6 Genome portal inspection 100
5.7 Analysing novel genes 101
5.8 Conclusions and prospects 102
References 103
6 Comparative genomics 105
Martin S. Taylor and Richard R. Copley
6.1 Introduction 105
6.2 The genomic landscape 106
6.3 Concepts 109
6.4 Practicalities 113
6.5 Technology 118
6.6 Applications 132
6.7 Challenges and future directions 137
6.8 Conclusion 138
References 139
Section III Bioinformatics for Genetic Study Design and Analysis 145
7 Identifying mutations in single gene disorders 147
David P. Kelsell, Diana Blaydon and Charles A. Mein
7.1 Introduction 147
7.2 Clinical ascertainment 147
7.3 Genome-wide mapping of monogenic diseases 148
7.4 The nature of mutation in monogenic diseases 152
7.5 Considering epigenetic effects in mendelian traits 160
7.6 Summary 162
References 162
8 From Genome Scan to Culprit Gene 165
Ian C. Gray
8.1 Introduction 165
8.2 Theoretical and practical considerations 166
8.3 A stepwise approach to locus refinement and candidate gene identification 176
8.4 Conclusion 180
8.5 A list of the software tools and Web links mentioned in this chapter 181
References 182
9 Integrating Genetics, Genomics and Epigenomics to Identify Disease Genes 185
Michael R. Barnes
9.1 Introduction 185
9.2 Dealing with the (draft) human genome sequence 186
9.3 Progressing loci of interest with genomic information 187
9.4 In silico characterization of the IBD5 locus – a case study 191
9.5 Drawing together biological rationale – hypothesis building 209
9.6 Identification of potentially functional polymorphisms 211
9.7 Conclusions 212
References 213
10 Tools for statistical genetics 217
Aruna Bansal, Charlotte Vignal and Ralph McGinnis
10.1 Introduction 217
10.2 Linkage analysis 217
10.3 Association analysis 223
10.4 Linkage disequilibrium 229
10.5 Quantitative trait locus (QTL) mapping in experimental crosses 235
10.6 Closing remarks 239
References 241
Section IV Moving From Associated Genes to Disease Alleles 247
11 Predictive functional analysis of polymorphisms: An overview 249
Mary Plumpton and Michael R. Barnes
11.1 Introduction 249
11.2 Principles of predictive functional analysis of polymorphisms 252
11.3 The anatomy of promoter regions and regulatory elements 256
11.4 The anatomy of genes 258
11.5 Pseudogenes and regulatory mRNA 266
11.6 Analysis of novel regulatory elements and motifs in nucleotide sequences 266
11.7 Functional analysis of non-synonymous coding polymorphisms 268
11.8 Integrated tools for functional analysis of genetic variation 273
11.9 A note of caution on the prioritization of in silico predictions for further laboratory investigation 275
11.10 Conclusions 275
References 276
12 Functional in silico analysis of gene regulatory polymorphism 281
Chaolin Zhang, Xiaoyue Zhao, Michael Q. Zhang
12.1 Introduction 281
12.2 Predicting regulatory regions 282
12.3 Modelling and predicting transcription factor-binding sites 288
12.4 Predicting regulatory elements for splicing regulation 295
12.5 Evaluating the functional importance of regulatory polymorphisms 300
References 302
13 Amino-acid properties and consequences of substitutions 311
Matthew J. Betts and Robert B. Russell
13.1 Introduction 311
13.2 Protein features relevant to amino-acid behaviour 312
13.3 Amino-acid classifications 316
13.4 Properties of the amino acids 318
13.5 Amino-acid quick reference 321
13.6 Studies of how mutations affect function 334
13.7 A summary of the thought process 339
References 340
14 Non-coding RNA bioinformatics 343
James R. Brown, Steve Deharo, Barry Dancis, Michael R. Barnes and Philippe Sanseau
14.1 Introduction 343
14.2 The non-coding (nc) RNA universe 344
14.3 Computational analysis of ncRNA 349
14.4 ncRNA variation in disease 356
14.5 Assessing the impact of variation in ncRNA 362
14.6 Data resources to support small ncRNA analysis 363
14.7 Conclusions 363
References 364
Section V Analysis at the Genetic and Genomic Data Interface 369
15 What are microarrays? 371
Catherine A. Ball and Gavin Sherlock
15.1 Introduction 371
15.2 Principles of the application of microarray technology 373
15.3 Complementary approaches to microarray analysis 377
15.4 Differences between data repository and research database 377
15.5 Descriptions of freely available research database packages 377
References 385
16 Combining quantitative trait and gene-expression data 389
Elissa J. Chesler
16.1 Introduction: the genetic regulation of endophenotypes 389
16.2 Transcript abundance as a complex phenotype 390
16.3 Scaling up genetic analysis and mapping models for microarrays 394
16.4 Genetic correlation analysis 397
16.5 Systems genetic analysis 400
16.6 Using expression QTLs to identify candidate genes for the regulation of complex phenotypes 403
16.7 Conclusions 408
References 408
17 Bioinformatics and cancer genetics 413
Joel Greshock
17.1 Introduction 413
17.2 Cancer genomes 414
17.3 Approaches to studying cancer genetics 415
17.4 General resources for cancer genetics 418
17.5 Cancer genes and mutations 420
17.6 Copy number alterations in cancer 425
17.7 Loss of heterozygosity in cancer 431
17.8 Gene-expression data in cancer 432
17.9 Multiplatform gene target identification 435
17.10 The epigenetics of cancer 438
17.11 Tumour modelling 438
17.12 Conclusions 439
References 439
18 Needle in a haystack? Dealing with 500 000
SNP genome scans 447
Michael R. Barnes and Paul S. Derwent
18.1 Introduction 447
18.2 Genome scan analysis issues 449
18.3 Ultra-high-density genome-scanning technologies 459
18.4 Bioinformatics for genome scan analysis 469
18.5 Conclusions 489
References 490
19 A bioinformatics perspective on genetics in drug discovery and development 495
Christopher Southan, Magnus Ulvsbäck and Michael R. Barnes
19.1 Introduction 495
19.2 Target genetics 498
19.3 Pharmacogenetics (PGx) 508
19.4 Conclusions: toward ‘personalized medicine’ 525
References 525
Appendix I 529
Appendix II 531
Index 537
